VOOZH about

URL: https://dev.to/datanestdigital/data-quality-framework-a-comprehensive-guide-2026-2j4i

⇱ Data Quality Framework - DEV Community

Data Quality Framework

Trust your data. A pluggable quality engine with built-in checks for completeness,
uniqueness, validity, freshness, and consistency — plus automated reporting to Slack,
HTML, and Delta Lake.

By Datanest Digital | Version 1.0.0 | $49

What You Get

  • Quality Engine — Rule-based engine that loads checks from YAML, executes them against any Spark DataFrame, aggregates results, and produces structured reports
  • 6 Check Types — Completeness (null/empty), uniqueness (duplicates), validity (regex, range, enum), freshness (staleness), consistency (cross-table), and custom (arbitrary SQL expressions)
  • 3 Reporters — Slack webhook notifications, standalone HTML reports, and Delta Lake audit table writer for historical trending
  • YAML Configuration — Define rules and thresholds in human-readable YAML; no code changes needed to add new checks
  • Databricks Notebook — Ready-to-run notebook for executing quality checks as a scheduled job
  • Strategy Guide — Best practices for implementing data quality at scale

File Tree 

data-quality-framework/
├── README.md
├── manifest.json
├── LICENSE
├── src/
│ ├── quality_engine.py # Core engine: load, execute, report
│ ├── checks/
│ │ ├── completeness.py # Null/empty field checks
│ │ ├── uniqueness.py # Duplicate detection
│ │ ├── validity.py # Regex, range, enum validation
│ │ ├── freshness.py # Data staleness checks
│ │ ├── consistency.py # Cross-table consistency
│ │ └── custom.py # Arbitrary SQL expression checks
│ └── reporters/
│ ├── slack_reporter.py # Slack webhook notifications
│ ├── html_reporter.py # Standalone HTML report
│ └── delta_reporter.py # Delta Lake audit table writer
├── configs/
│ ├── quality_rules.yaml # Rule definitions
│ └── thresholds.yaml # Pass/warn/fail thresholds
├── notebooks/
│ └── run_quality_checks.py # Databricks notebook
├── tests/
│ ├── conftest.py # Shared fixtures
│ └── test_quality_engine.py # Unit tests
└── guides/
 └── data-quality-strategy.md # Best practices guide

Getting Started

1. Define your quality rules

Edit configs/quality_rules.yaml to specify which checks to run:

rules:
 - name: "customer_email_not_null"
 table: "analytics.silver.customers"
 check_type: "completeness"
 columns: ["email"]
 threshold: 0.99 # 99% must be non-null

 - name: "order_id_unique"
 table: "analytics.silver.orders"
 check_type: "uniqueness"
 columns: ["order_id"]
 threshold: 1.0 # 100% unique

2. Run quality checks 

from src.quality_engine import QualityEngine

engine = QualityEngine.from_config(
 rules_path="configs/quality_rules.yaml",
 thresholds_path="configs/thresholds.yaml",
)

# Execute all rules and get a report
report = engine.run_all()
print(report.summary())

# Check if all rules passed
if not report.passed:
 print(f"FAILED: {report.failed_count} of {report.total_count} checks failed")

3. Send notifications 

from src.reporters.slack_reporter import SlackReporter
from src.reporters.delta_reporter import DeltaReporter

# Send Slack alert for failures
slack = SlackReporter(webhook_url="https://hooks.slack.com/services/T.../B.../xxx")
slack.send(report)

# Persist results to Delta Lake for trending
delta_reporter = DeltaReporter(audit_table="analytics.ops.quality_audit")
delta_reporter.write(report)

Requirements

  • Databricks Runtime 13.3 LTS or later
  • Apache Spark 3.4+
  • Delta Lake 2.4+
  • Python 3.10+
  • requests (for Slack reporter)

Architecture 

┌──────────────────┐ ┌────────────────────┐
│ quality_rules │────▶│ Quality Engine │
│ .yaml │ │ │
└──────────────────┘ │ 1. Load rules │
┌──────────────────┐ │ 2. Execute checks │
│ thresholds │────▶│ 3. Aggregate │
│ .yaml │ │ 4. Report │
└──────────────────┘ └─────────┬──────────┘
 │
 ┌────────────────────┼────────────────────┐
 ▼ ▼ ▼
 ┌────────────────┐ ┌────────────────┐ ┌────────────────┐
 │ Slack Reporter │ │ HTML Reporter │ │ Delta Reporter │
 │ (webhook) │ │ (standalone) │ │ (audit table) │
 └────────────────┘ └────────────────┘ └────────────────┘

Related Products

This is 1 of 11 resources in the Data Pipeline Pro toolkit. Get the complete [Data Quality Framework] with all files, templates, and documentation for $49.

Get the Full Kit →

Or grab the entire Data Pipeline Pro bundle (11 products) for $169 — save 30%.

Get the Complete Bundle →

Related Articles